Acute and Critical Care

Original Articles

Pediatrics
Clinical implications of discrepancies in predicting pediatric mortality between Pediatric Index of Mortality 3 and Pediatric Logistic Organ Dysfunction-2: Eui Jun Lee, Bongjin Lee, You Sun Kim, Yu Hyeon Choi, Young Ho Kwak, June Dong Park; Acute Crit Care. 2022;37(3):454-461. Published online July 29, 2022; DOI: https://doi.org/10.4266/acc.2021.01480

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Abstract PDF: Background
Pediatric Index of Mortality 3 (PIM 3) and Pediatric Logistic Organ Dysfunction-2 (PELOD-2) are validated tools for predicting mortality in children. Research suggests that these tools may have different predictive performance depending on patient group characteristics. Therefore, we designed this study to identify the factors that make the mortality rates predicted by the tools different.
Methods
This retrospective study included patients (<18 years) who were admitted to a pediatric intensive care unit from July 2017 to May 2019. After defining the predicted mortality of PIM 3 minus the predicted mortality rate of PELOD-2 as “difference in mortality prediction,” the clinical characteristics significantly related to this were analyzed using multivariable regression analysis. Predictive performance was analyzed through the Hosmer-Lemeshow test and area under the receiver operating characteristic curve (AUROC).
Results
In total, 945 patients (median [interquartile range] age, 3.0 [0.0–8.0] years; girls, 44.7%) were analyzed. The Hosmer-Lemeshow test revealed AUROCs of 0.889 (χ2=10.187, P=0.313) and 0.731 (χ2=6.220, P=0.183) of PIM 3 and PELOD-2, respectively. Multivariable linear regression analysis revealed that oxygen saturation, partial pressure of CO2, base excess, platelet counts, and blood urea nitrogen levels were significant factors. Patient condition-related factors such as cardiac bypass surgery, seizures, cardiomyopathy or myocarditis, necrotizing enterocolitis, cardiac arrest, leukemia or lymphoma after the first induction, bone marrow transplantation, and liver failure were significantly related (P<0.001).
Conclusions
Both tools predicted observed mortality well; however, caution is needed in interpretation as they may show different prediction results in relation to specific clinical characteristics.

Pulmonary
Performance of APACHE IV in Medical Intensive Care Unit Patients: Comparisons with APACHE II, SAPS 3, 216 and MPM₀ III: Mihye Ko, Miyoung Shim, Sang-Min Lee, Yujin Kim, Soyoung Yoon; Acute Crit Care. 2018;33(4):216-221. Published online November 21, 2018; DOI: https://doi.org/10.4266/acc.2018.00178

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14 Web of Science
15 Crossref

Background
In this study, we analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and Mortality Probability Model (MPM)0 III in order to determine which system best implements data related to the severity of medical intensive care unit (ICU) patients.
Methods
The present study was a retrospective investigation analyzing the discrimination and calibration of APACHE II, APACHE IV, SAPS 3, and MPM0 III when used to evaluate medical ICU patients. Data were collected for 788 patients admitted to the ICU from January 1, 2015 to December 31, 2015. All patients were aged 18 years or older with ICU stays of at least 24 hours. The discrimination abilities of the three systems were evaluated using c-statistics, while calibration was evaluated by the Hosmer-Lemeshow test. A severity correction model was created using logistics regression analysis.
Results
For the APACHE IV, SAPS 3, MPM0 III, and APACHE II systems, the area under the receiver operating characteristic curves was 0.745 for APACHE IV, resulting in the highest discrimination among all four scoring systems. The value was 0.729 for APACHE II, 0.700 for SAP 3, and 0.670 for MPM0 III. All severity scoring systems showed good calibrations: APACHE II (chi-square, 12.540; P=0.129), APACHE IV (chi-square, 6.959; P=0.541), SAPS 3 (chi-square, 9.290; P=0.318), and MPM0 III (chi-square, 11.128; P=0.133).
Conclusions
APACHE IV provided the best discrimination and calibration abilities and was useful for quality assessment and predicting mortality in medical ICU patients.

Citations

Citations to this article as recorded by

Predicting Hospital Survival in Patients Admitted to ICU with Pulmonary Embolism
Martin J. Ryll, Aurelia Zodl, Toby N. Weingarten, Alejandro A. Rabinstein, David O. Warner, Darrell R. Schroeder, Juraj Sprung
Journal of Intensive Care Medicine.2024; 39(5): 455. CrossRef
Relationship between Patient Classification System and APACHE II Scores, and Mortality Prediction in a Surgical Intensive Care Unit
U Ri Go, Sung-Hyun Cho
Journal of Korean Academy of Nursing Administration.2024; 30(1): 67. CrossRef
Utilidad del uso del modelo MPM-II para predecir riesgo de mortalidad en comparación con SAPS-II en pacientes adultos en la unidad de cuidados intensivos
Perla Marlene Guzmán Ramírez
Acta Médica Grupo Ángeles.2023; 21(2): 115. CrossRef
Plasma and Urinary Biomarkers Improve Prediction of Mortality through 1 Year in Intensive Care Patients: An Analysis from FROG-ICU
Beth A. Davison, Christopher Edwards, Gad Cotter, Antoine Kimmoun, Étienne Gayat, Agnieszka Latosinska, Harald Mischak, Koji Takagi, Benjamin Deniau, Adrien Picod, Alexandre Mebazaa
Journal of Clinical Medicine.2023; 12(9): 3311. CrossRef
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Se Hun Kim, Ki Hoon Kim
Surgery.2023; 174(3): 611. CrossRef
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Vedran Premužić, Jakša Babel, Danilo Gardijan, Ivana Lapić, Rajka Gabelica, Zvonimir Ostojić, Marin Lozić, Gordana Pavliša, Maja Hrabak, Josip Knežević, Dunja Rogić, Slobodan Mihaljević
Therapeutic Apheresis and Dialysis.2022; 26(2): 316. CrossRef
Relation between red blood cell distribution width and acute kidney injury in patients with sepsis
Marina Larissa Vettorello Ramires, Manoela Fidelis Batista Leite, Daniel Zu Yow Lo, Leonardo Bonilla da Silveira, Leonardo José Rolim Ferraz, Andreia Pardini, Araci Massami Sakashita, Andrea Tiemi Kondo, Guilherme Benfatti Olivato, Marcelino de Souza Durã
Einstein (São Paulo).2022;[Epub] CrossRef
Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach
Elena Caires Silveira, Soraya Mattos Pretti, Bruna Almeida Santos, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva, Fabrício Freire de Melo
World Journal of Critical Care Medicine.2022; 11(5): 317. CrossRef
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Mahima Lakhanpal, Debpriya Sarkar, Ritesh Kumar, Isha Yadav
Anesthesia: Essays and Researches.2022; 16(3): 296. CrossRef
Phase Angle and Frailty are Important Prognostic Factors in Critically Ill Medical Patients: A Prospective Cohort Study
S.J. Ko, J. Cho, S.M. Choi, Y.S. Park, C.-H. Lee, S.-M. Lee, C.-G. Yoo, Y.W. Kim, Jinwoo Lee
The Journal of nutrition, health and aging.2021; 25(2): 218. CrossRef
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Eduardo A. Trujillo Rivera, Anita K. Patel, James M. Chamberlain, T. Elizabeth Workman, Julia A. Heneghan, Douglas Redd, Hiroki Morizono, Dongkyu Kim, James E. Bost, Murray M. Pollack
Pediatric Critical Care Medicine.2021; 22(1): e33. CrossRef
Validation of the Acute Physiology and Chronic Health Evaluation (APACHE) II and IV Score in COVID-19 Patients
Jeroen Vandenbrande, Laurens Verbrugge, Liesbeth Bruckers, Laurien Geebelen, Ester Geerts, Ina Callebaut, Ine Gruyters, Liesbeth Heremans, Jasperina Dubois, Bjorn Stessel, Edward A Bittner
Critical Care Research and Practice.2021; 2021: 1. CrossRef
Relationship Between Mean Vancomycin Trough Concentration and Mortality in Critically Ill Patients: A Multicenter Retrospective Study
Yanli Hou, Jiajia Ren, Jiamei Li, Xuting Jin, Ya Gao, Ruohan Li, Jingjing Zhang, Xiaochuang Wang, Xinyu Li, Gang Wang
Frontiers in Pharmacology.2021;[Epub] CrossRef
Blood purification therapy with a hemodiafilter featuring enhanced adsorptive properties for cytokine removal in patients presenting COVID-19: a pilot study
Gianluca Villa, Stefano Romagnoli, Silvia De Rosa, Massimiliano Greco, Marco Resta, Diego Pomarè Montin, Federico Prato, Francesco Patera, Fiorenza Ferrari, Giuseppe Rotondo, Claudio Ronco
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Nursing
The Inter-Rater Reliability of Simplified Acute Physiology Score 3 (SAPS3) among Intensive Care Unit Nurses: Jun Hyun Kim, Ji Yeon Kim, Wonil Kim, Kyung Woo Kim, Sang-il Lee, Kyung-Tae Kim, Jang Su Park, Won Joo Choe, Jung Won Kim; Korean J Crit Care Med. 2015;30(1):8-12. Published online February 28, 2015; DOI: https://doi.org/10.4266/kjccm.2015.30.1.8

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Abstract PDF: Background
Simplified acute physiology score 3 (SAPS3) was developed in 2005 to evaluate intensive care unit (ICU) performance and to predict patient mortality or disease severity. The score is usually calculated by doctors, but it requires substantial human resources. And many nurse-lead studies use this scoring system. In the present study, we examined the inter-rater reliability of SAPS3 among nurses in an ICU. Methods: Five ICU nurses who worked in an ICU for a mean length of 7.8 years were educated for 2 hours about SAPS3 score and its components. Each nurse scored 26 patients, and the intraclass correlation coefficient (ICC) of the total scores and each subset were evaluated. Results: The ICC (95% confidence interval) of SAPS3 score was 0.89 (0.82-0.95), that of subset I was 0.90 (0.82-0.95), subset II was 0.54 (0.35-0.73), and subset III was 0.95 (0.91-0.97). The ICC of predicted mortality was 0.91 (0.85-0.96). Conclusions: The ICC of SAPS3 score and predicted mortality among ICU nurses were reliable. According to these ICC values, SAPS3 score is a reliable scale to be used by nurses. The ICC of subset II was lower than those of the other subsets, suggesting that education of SAPS3 should focus on the definition of each subset II component.

Severe Health-care Associated Pneumonia among the Solid Cancer Patients on Chemotherapy: Maeng Real Park, So Young Park, Kyeongman Jeon, Won Jung Koh, Man Pyo Chung, Hojoong Kim, O Jung Kwon, Gee Young Suh, Jin Seok Ahn, Myung Ju Ahn, Ho Yeong Lim; Korean J Crit Care Med. 2009;24(3):140-144.; DOI: https://doi.org/10.4266/kjccm.2009.24.3.140

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Abstract PDF: BACKGROUND
There are only inadequate studies on the characteristics of severe pneumonia in the patients who have solid cancer and who are treated with cytotoxic chemotherapy and also on the usefulness of the various severity index scores.
METHODS
We retrospectively reviewed 31 patients who were treated with cytotoxic chemotherapy because of solid cancer and who were admitted to the medical ICU at Samsung Medical Center from April 2007 to August 2008.
RESULTS
The median age of the 31 patients was 64 years old (34-79). The types of solid cancer were lung cancer (19, 61.3%), gastroesophageal cancer (4, 12.9%), breast cancer (2, 6.5%), liver cancer (1, 3.2%), ovarian cancer (1, 3.2%) and other types of cancer (4, 12.9%). The hospital mortality rate was 64.5%. We were able to determine the pathogen of 19 (61.3%) patients; S. pneumoniae (6), S. aureus (3), Candida species (3), P. aeruginosa (2), K. pneumoniae (1), Pneumocystis jiroveci (1) and others (3). There were no statistically differences of the laboratory data and severity index scores (PSI, CURB-65, APACHE II, SOFA, SAPS 3) between the survivors and nonsurvivors, except the P/F ratio.
CONCLUSIONS
The hospital mortality rate of severe pneumonia in patients who had solid cancer and who received cytotoxic chemotherapy was high. The major pathogen was S. pneumoniae. The severity indexes for general pneumonia were not useful to these patients.

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